IDEAS home Printed from https://ideas.repec.org/a/taf/tjsmxx/v16y2022i2p182-193.html
   My bibliography  Save this article

Load-balancing scheduling of simulation tasks based on a static-dynamic hybrid algorithm

Author

Listed:
  • Xiashuang Wang
  • Ni Li
  • Guanghong Gong
  • Xiao Song
  • Yanqi Guo

Abstract

A scheduling algorithm is crucial for running a simulation model so that tasks can be performed efficiently.The traditionally used blade-based parallel engine system cannot be adapted to a new simulation model. This study proposed a combined dynamic priority and static method, that is, a hybrid load-balancing scheduling (HLB) algorithm. The algorithm is given priority according to the operating cycle of the model and system steps. The experimental results demonstrated that the algorithm outperformed the earliest deadline first and the time-stepped load-balancing scheduling algorithms. The results also demonstrated that the HLBhad a higher real-time operating efficiency than the other algorithms under a lower overhead guarantee. The HLB algorithm causedbetter performance while maintaining computation and communication efficiency. Simultaneously, the utilisation rate of the central processing unit was around 35%.The further study should be enhanced to generalise it so that it could be applied to incorporate load balancing.

Suggested Citation

  • Xiashuang Wang & Ni Li & Guanghong Gong & Xiao Song & Yanqi Guo, 2022. "Load-balancing scheduling of simulation tasks based on a static-dynamic hybrid algorithm," Journal of Simulation, Taylor & Francis Journals, vol. 16(2), pages 182-193, March.
  • Handle: RePEc:taf:tjsmxx:v:16:y:2022:i:2:p:182-193
    DOI: 10.1080/17477778.2020.1772023
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/17477778.2020.1772023
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/17477778.2020.1772023?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:tjsmxx:v:16:y:2022:i:2:p:182-193. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/tjsm .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.